1、创建数组 numpy.array:创建新的NumPy数组 # Create an array using np.array() arr = np.array([1, 2, 3, 4, 5]) print(arr) Ouput: [1 2 3 4 5] numpy.zeros:创建一个以零填充的数组。 # Create a 2-dimensional array of zeros arr = np.zeros((3, 4)) [[0. 0. 0. 0.] [0...
arr= np.array([2,1,3,2,1,4,5,4]) # Get the unique elements of the array unique_values = np.unique(arr) [12345] arr=np.array([1, 2, 3, np.nan, 5]) # Create a masked array by masking the invalid values masked_arr=ma.masked_invalid(arr) [12 3 5] numpy.apply_alon...
arr = np.array([1, 2, 3, np.nan, 5]) # Create a masked array by masking the invalid values masked_arr = ma.masked_invalid(arr) [1 2 3 5] numpy.apply_along_axis:沿着数组的特定轴应用函数。 numpy.wheres:一个条件函数,根据给定条件返回数组中满足条件的元素的索引或值。 代码语言:javascr...
1、创建数组 numpy.array:创建新的NumPy数组 # Create an array using np.array() arr = np.array([1, 2, 3, 4, 5]) print(arr) Ouput: [1 2 3 4 5] numpy.zeros:创建一个以零填充的数组。 # Create a 2-dimensional array of zeros arr = np.zeros((3, 4)) [[0. 0. 0. 0.] [0...
numpy.array:创建新的NumPy数组 # Create anarrayusingnp.array()arr = np.array([1,2,3,4,5])print(arr)Ouput: [12345] numpy.zeros:创建一个以零填充的数组。 # Create a 2-dimensional array of zerosarr = np.zeros((3,4))[[0. 0. 0. 0...
numpy.array:创建新的NumPy数组 # Create an array using np.array() arr = np.array([1, 2, 3, 4, 5]) print(arr) Ouput: [1 2 3 4 5] numpy.zeros:创建一个以零填充的数组。 # Create a 2-dimensional array of zeros arr = np.zeros((3, 4)) ...
# Create an array using np.array() arr = np.array([1, 2, 3, 4, 5]) print(arr) Ouput: [1 2 3 4 5] 1. 2. 3. 4. 5. numpy.zeros:创建一个以零填充的数组。 AI检测代码解析 # Create a 2-dimensional array of zeros
条件索引 Boolean Masking 1np.random.seed(42)2a=np.random.randint(0, 21, 15)3a[98]:array([ 6, 19, 14, 10, 7, 20, 6, 18, 10, 10, 20, 3, 7, 2, 20])1c=a%3==02c.dtype3print(c)4a[c][ True False False False False False True True False False False True False False ...
numpy.array:创建新的NumPy数组 # Create an array using np.array()arr= np.array([1,2,3,4,5])print(arr)Ouput:[1 2 3 4 5] numpy.zeros:创建一个以零填充的数组。 # Create a 2-dimensional array of zerosarr= np.zeros((3,4))[[0. 0. 0. 0.][0. 0. 0. 0.][0. 0. 0. 0...
Extract from the array np.array([3,4,6,10,24,89,45,43,46,99,100]) with Boolean masking all the number which are not divisible by 3 which are divisible by 5 which are divisible by 3 and 5 which are divisible by 3 and set them to 42 ...